Sydney: M4 Smart Motorway System
Aimsun Live is the traffic prediction software at the heart of the NSW Government’s $600m M4 Smart Motorway project. The project uses real-time data, communications and ITS to improve traffic flow.
Land Transport Authority of Singapore
Singapore
Jan 2018 to Apr 2019
AIMSUN LIVE
SCATSIM
SINGAPORE
The purpose of the technology trial was to evaluate whether the latest traffic simulation-based solutions and prediction capabilities of Aimsun Live, can assist Land Transport Authority of Singapore (LTA) to manage traffic condition better, faster and more effective, and support decision making related to current and future traffic challenges in the following ways:
An area covering the central districts of Singapore was chosen for the technology trial, which comprises two major expressways and 100 signalised junctions, supporting a residential population of 200,000 over 18.2 km2.
Aimsun Live is a vehicle-based simulation decision support system, complemented with predictive analytics system, for real-time traffic forecasting and management.
The Aimsun team first developed a baseline model of the selected area in an “offline” environment, drawing initial network parameters, traffic signal plans, public transport information and traffic demand matrices from LTA’s existing asset management systems and strategic planning models. Detailed calibration and validation were carried out at mesoscopic level, as well as at microscopic level, where necessary, before connecting to Aimsun Live, using historical traffic data to create a pseudo realtime environment for “live” traffic simulation and prediction.
To assess the full capabilities of Aimsun Live as a decision support tool, the Aimsun team worked very closely with different user groups in LTA to define a range of events/ incidents and their respective actual and possible alternative traffic management response plans, to understand the model’s response to different situations in real-time.
This is also the first trial to link Aimsun Live to SCATSim in Singapore to emulate the actual adaptive traffic signal control of the SCATS system, for a more accurate representation of the real-time traffic signal operations.
LTA is able to have a deep understanding on the benefit of a proactive traffic management tool that Aimsun Live can offer. It provides a platform where LTA is able to continuously monitor the road network performance based on a rich and ever-expanding real-time data network, coupled with fast simulation time for short-term forecasting of the impact caused by any sudden events / incidents, to determine the best traffic management strategy with confidence in no time.
It also offers a common and integrated traffic simulation platform that allows the various users within LTA to select an area of interest and export to Aimsun Next for macroscopic, mesoscopic, microscopic or hybrid offline modelling, to support historical event evaluation or other transport studies.
Although Aimsun Live is a standard, off-the-shelf commercial solution, it can slot into any traffic control centre with interfaces, fully customisable to LTA’s existing traffic management system e.g. connection to real-time data feeds. The rich simulation statistics mean that different indicators can be customised to suit the specific needs of different user groups, both visually and in log for record and post-event evaluation.
As a collaboration with LTA, many discussions and meetings were held throughout the trial to communicate the development process, seek engagement of inputs / features and provide training and technical support to the various users to ensure the use of the tool meets their requirements and expectations.
Aimsun Live is the traffic prediction software at the heart of the NSW Government’s $600m M4 Smart Motorway project. The project uses real-time data, communications and ITS to improve traffic flow.
Queensland Department of Transport and Main Roads commissioned an operational Aimsun Live implementation to assess its accuracy and potential value for future operations planning.
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